10-Week DATAX: The Industry Crossover

Master the fundamentals of data science, AI, and machine learning through hands-on projects and real-world applications

10 Intensive Weeks
Real-World Projects
Industry Mentors
Portfolio Ready

Comprehensive Curriculum

From Python fundamentals to advanced AI deployment, master every aspect of modern data science

Week 1
Python Foundations & DevOps Basics

Learning Objectives

  • Master Python fundamentals: async, CLI, env setup
  • Understand Git workflows & basic CI/CD
  • Manage API keys securely

Topics & Subtopics

Python Speedrun
  • Comprehensions & generators
  • Asyncio for better performance
  • CLI UIs using Rich
Git & CI/CD
  • Git-flow essentials
  • GitHub Actions basics
API Key Management
  • .env usage with python-dotenv
  • Rate-limiting strategies
Week 2
Prompting & Generative AI Logic

Learning Objectives

  • Understand prompt structure & design
  • Create intelligent personas with language models
  • Test and evaluate prompt responses

Topics & Subtopics

Prompt Structure
  • Roles: system, user, assistant
  • Chain-of-thought prompting
  • Sampling settings: temperature, top-k/p
Persona Crafting
  • Tone/style templates
  • Dynamic role switching
Prompt Evaluation
  • Win/loss scoring with AI
  • UI elements for testing responses
Week 3
Data Workflows with LangChain

Learning Objectives

  • Learn how to use chains and agents effectively
  • Build logic-based workflows
  • Improve performance with caching

Topics & Subtopics

LangChain Core Concepts
  • Understanding Chains and Agents
  • Tool integration and callbacks
Workflow Branching
  • Conditional routing
  • Error handling strategies
Caching Methods
  • Redis usage
  • Response serialization
Week 4
Retrieval-Augmented Generation (RAG)

Learning Objectives

  • Use embeddings to enhance AI memory
  • Store and query data using vector databases
  • Implement smart filtering

Topics & Subtopics

Embeddings
  • Comparing OpenAI and SentenceTransformers
  • Chunking strategies
Vector Databases
  • FAISS basics
  • Intro to Pinecone
Filtering & Cleanup
  • Time-to-live (TTL)
  • Trust scoring mechanisms
Week 5
Multi-Modal Intelligence

Learning Objectives

  • Combine text, vision, and audio models
  • Work with transcription and TTS tools
  • Build interactive interfaces

Topics & Subtopics

Vision Models
  • CLIP & SAM introduction
Audio Intelligence
  • Using Whisper for transcription
  • Voice synthesis tricks
UI Development
  • Streamlit/Gradio basics
  • Real-time updates
Week 6
Web Data & API Integration

Learning Objectives

  • Use headless browsers for scraping
  • Connect and combine APIs
  • Create modular AI tools

Topics & Subtopics

Web Scraping Tools
  • Playwright vs. Selenium
  • Handling captcha ethically
API Consumption
  • Integrating SerpAPI, NewsAPI
  • Using WolframAlpha
Tool Creation
  • LangChain Tool SDK overview
Week 7
Evaluation & QA Automation

Learning Objectives

  • Apply reasoning methods to agent actions
  • Build feedback loops into workflows
  • Create automated test pipelines

Topics & Subtopics

ReAct Methodology
  • Thought/action separation
Feedback Loops
  • Reflexion techniques
  • Logging successes/failures
QA Pipelines
  • Prompt and handler testing
  • pytest basics
Week 8
Lightweight Deployment

Learning Objectives

  • Deploy models on limited hardware
  • Use Docker for scalable apps
  • Integrate IoT sensor inputs

Topics & Subtopics

Model Optimization
  • ONNX, bitsandbytes
Containerization
  • Multi-arch Docker images
  • Kubernetes on Raspberry Pi (K3s)
IoT Triggers
  • MQTT protocols
  • Webhook integration
Week 9
Plugin Ecosystems

Learning Objectives

  • Design plugin systems for modular apps
  • Track usage and engagement
  • Prototype monetization options

Topics & Subtopics

Plugin Design
  • Python vs. JS plugins
  • Versioning and security
Discovery & UIs
  • Install flows
  • Metering usage
Payments & APIs
  • Micro-payment logic with Stripe
  • API-based pricing models
Week 10
Final Projects & Pitching

Learning Objectives

  • Collaborate on real-world data science builds
  • Present ideas with clarity
  • Handle feedback and iteration

Activities

Project Sprint
  • Role assignments: PM, ML, DevOps, UI
  • Build and polish phase
Demo Prep
  • Presentation skills
  • Storytelling for tech demos
Showcase
  • Peer and mentor reviews
  • Rewards and recognition

DATAX: Industry Crossover

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